Substantial evidence has shown that there is significant genetic component to the susceptibility of Chronic Obstructive Pulmonary Disease (COPD). However, only a small proportion of heritability can be explained by the genetic factors already discovered. One hypothesis is that genetic interactions play an important role in disease susceptibility as genetic interactions have been confirmed in several studies of complex disease. However, current methods for sequencing analysis do not consider the effect of genetic interaction. The objective of this proposal is to develop novel approaches for next-generation sequencing data by incorporating the effects of genetic interactions to identify genetic determinants associated with complex diseases, COPD in particular. Since network is a natural representation of interactions, the proposed approaches will be based on weighted networks representing genetic interactions. Gene-based and pathway-based association methods will be developed in Aim 1. A software toolset implementing the proposed methods and existing methods for the identification of gene-gene interactions will be provided for public access in Aim 2. In Aim 3, we will analyze the whole-exome sequencing data of 399 selected subjects from the COPDGene study to identify candidate genes and pathways in COPD using the proposed and existing methods, with validations using an independent whole-exome sequencing data of 600 subjects. We will also integrate the genetic network constructed with co- expression network and protein-protein interaction information to identify candidate gene-set modules. In sum, this proposal will help to elucidate our understanding of the genetic architecture of COPD, which will be beneficial to the improvements in prevention, diagnosis and treatment.